Field
Embodiments of the present disclosure generally relate to techniques for analyzing digital images. More specifically, embodiments presented herein provide techniques for extracting a robust set of features from a high-resolution image in order to generate a low-dimensional sample of the image for analysis.
Description of the Related Art
Image analytics generally refers to approaches that programmatically evaluate a stream of images for a variety of applications, such as in video surveillance, industrial control systems, and the like. An image analytics system may be configured to detect a set of pre-defined patterns in a successive stream of images. The pre-defined patterns may be hard-coded into the image analytics system (or the system may train itself based on provided definitions or rules). The image analytics system identifies abnormalities in the image sequences based on the pre-defined rules and alerts an administrator of the abnormalities. For example, in an industrial setting, an image analytics system may evaluate frames of a thermographic camera for changes in color, shape, or gradients that deviate from explicitly defined patterns.
However, many image analytics systems require a significant amount of computing resources to process raw image data. For example, evaluating sequences of high-resolution images may consume substantial processor power, storage, and bandwidth. Given the cost of the resources, such systems are difficult to scale. Further, such an approach may be rigid for some image analytics systems. For example, in video surveillance, a camera may be fixed on a given scene. Analytics may be unable to detect unpredictable behavior occurring in that scene, particularly if the behavior is undefined. That is, unless a given behavior conforms to a pre-defined rule, an occurrence of the behavior can go undetected by the system. Even if the system trains itself to identify changes in image characteristics, the system requires rules to be defined in advance for what to identify.